Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China

Al-Shaibah, Bazel; Liu, Xingpeng; Zhang, Jiquan; Tong, Zhijun; Zhang, Mingxi; El-Zeiny, Ahmed M.; Faichia, Cheechouyang; Hussain, Muhammad; Tayyab, Muhammad

doi:10.3390/rs13091603

Cited by 17 publications

(5 citation statements)

References 53 publications

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“…Most previous studies concentrate mainly on monitoring the substances with optical properties in the water, such as chlorophyll [44][45][46] and CDOM [47]. Relatively few studies focused on components that lack optical properties in the water, e.g., TN, TP, NH 4 -N, and NO 3 -N [20]. This made it difficult to construct a higher precision remote sensing model.…”

Section: Discussionmentioning

confidence: 99%

“…Hyperspectral technology has been widely applied to water quality parameters with optical activity, such as Chl-a [10,11] PC [12,13], TSS [14,15], chromophoric dissolved organic matter (CDOM) [16], transparency [17], and turbidity [18]. Although some scholars have studied water quality parameters such as TN, TP, NH 4 -N, and NO 3 -N [19,20], it is also a great challenge to estimate the concentrations of total phosphorus, total nitrogen, ammonia nitrogen, and nitrate nitrogen in inland waters because the above parameters do not have optical activity in the perception wavelength.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Cao

Sun

et al. 2021

Water

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The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Cao

Sun

et al. 2021

Water

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“…The proposed methodology broadly involves four key steps: analysis of the archived field sample data, atmospheric correction of earth observation imagery, development and verification of a spectrally-stratified WQP algorithm and application of the algorithm for WQP mapping ( [10,11,36,38]).…”

Section: Methodsmentioning

confidence: 99%

“…• Extraction of image data over the sample sites: The average pixel value for a 3x3 to 5x5 pixel window around the sample site would be taken as the representative image value for TM/ETM+ images to reduce random noise[36]. However, a single pixel would be used for MODIS and MERIS due to their coarser spatial resolution relative to TM/ETM+ images.•Filtering out noisy pixel data:…”

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confidence: 99%

Earth Observation Techniques to Assess Water Quality Monitoring in the Murray Darling Basin of Australia

Forghani

Islam

Kazemi

2021

OJC

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The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll -Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projec...

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“…After shaking well and reaching the mark, a 125 mg/L sodium phosphate standard solution (calculated as p) was obtained. Using a pipette, 4,8,12,16,20,24,28,32,36,40,44,48,52,56, and 60 mL volumes of the standard solution were transferred to a 2 L black water tank to obtain samples with different concentrations for measurement.…”

Section: Laboratory Measurementsmentioning

confidence: 99%

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Shi,

Qiu,

et al. 2024

Water

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Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

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Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China

Cited by 17 publications

References 53 publications

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Earth Observation Techniques to Assess Water Quality Monitoring in the Murray Darling Basin of Australia

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

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